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Traffic predictiong using model-setting and analuzing to probability relativity and environment data

A technology of environmental data and data, applied in data processing applications, image data processing, geographic information databases, etc., can solve problems such as prediction model failures

Active Publication Date: 2006-05-24
MICROSOFT TECH LICENSING LLC
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0006] However, predictive models can fail when they are associated with atypical or unexpected events

Method used

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  • Traffic predictiong using model-setting and analuzing to probability relativity and environment data
  • Traffic predictiong using model-setting and analuzing to probability relativity and environment data
  • Traffic predictiong using model-setting and analuzing to probability relativity and environment data

Examples

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Embodiment Construction

[0036] The claimed subject matter is now described with reference to the drawings, wherein like numerals are used to refer to like elements throughout. In the following description, for purposes of explanation, numerous specific details are set forth in order to provide a thorough understanding of claimed subject matter. It may be evident, however, that the claimed subject matter may be practiced without these specific details. In other instances, well-known structures and devices are shown in block diagram form in order to facilitate describing the claimed subject matter.

[0037] As used in this application, the terms "component" and "system" mean a computer-related entity, ie, hardware, a combination of hardware and software, software, or software in execution. For example, a component may be, but is not limited to being, a process running on a processor, a processor, an object, an executable, a thread of execution, a program, and a computer. As an example, both an applic...

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Abstract

Systems and methods are described for constructing predictive models, based on statistical machine learning, that can make forecasts about traffic flows and congestions, based on an abstraction of a traffic system into a set of random variables, including variables that represent the amount of time until there will be congestion at key troublespots and the time until congestions will resolve. Observational data includes traffic flows and dynamics, and other contextual data such as the time of day and day of week, holidays, school status, the timing and nature of major gatherings such as sporting events, weather reports, traffic incident reports, and construction and closure reports. The forecasting methods are used in alerting, the display graphical information about predictions about congestion on desktop on mobile devices, and in offline and real-time automated route recommendations and planning.

Description

[0001] References to related applications [0002] This application claims priority to US Provisional Application No. 60 / 628,267, filed November 16, 2004, entitled SYSTEM AND METHOD FORPREDICTION AND PRESENTATION OF ATYPICAL EVENTS. This application also relates to Agent Docket No. MS311463.01 / MSFTP915US, entitled PRECOMPUTATION ANDTRANSMISSION OF TIME-DEPENDENT INFORMATION FOR VARYING ORUNCERTAIN RECEIPT TIMES (for variable or uncertain receipt time pre-computation and sending time-related information); Agent Docket No. MS311464.01 / MSFTP916US, entitled BUILDINGAND USINGPREDICTIVE MODELS FOR CURRENT AND FUTURE SURPRISES (Building and Using Predictive Models of Current and Future Unexpected Events); and Agent Volume MS311466.01 / MSFTP917US, entitled METHODS FOR AUTOMATED AND SEMIAUTOMATED COMPOSITIONOF VISUAL SEQUENCES, FLOWS, AND FLYOVERS BASED ON CONTENTAND CONTEXT (Methods for Automatic and Semi-Automatic Composition of Visual Sequences, Flows, and Interchanges Based on Content...

Claims

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Application Information

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IPC IPC(8): G06Q50/00
CPCG06Q10/06G06T19/003G06F16/29G06F16/4393G01C21/36G06T3/4092G01C21/3676
Inventor E·J·霍维茨J·T·阿帕基伯R·K·萨林
Owner MICROSOFT TECH LICENSING LLC
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